South Korea’s financial regulator has taken a major step toward modernizing oversight of digital asset markets by rolling out an artificial intelligence system designed to detect and analyze unfair trading practices in real time.
The Financial Supervisory Service (FSS) announced on August 20, 2026, that it had independently developed and deployed an AI-powered surveillance framework combining generative AI models with machine learning algorithms.
The initiative aims to address the challenges of monitoring thousands of tokens that trade around the clock across multiple domestic and international exchanges, a task that has strained limited investigative resources.
The new system builds on earlier AI tools introduced by the agency earlier in 2026.
In January, regulators created an algorithm capable of automatically flagging suspicious order activity and trading windows linked to potential price manipulation.
By April, they added functionality to identify clusters of connected accounts that may be working together.
The latest expansion integrates AI across the full surveillance pipeline, covering initial detection, deeper analysis, and preliminary assessment of whether formal probes are warranted.
A core strength of the platform lies in its ability to spot ultra-short-term pump-and-dump schemes.
Drawing on patterns observed in past cases, the system continuously screens real-time exchange data for anomalous price and volume movements.
It specifically targets tactics such as “racehorse” schemes, in which prices are sharply inflated during narrow time windows, and “cage” or “pen” schemes involving assets subject to temporary deposit or withdrawal restrictions.
When irregularities appear, generative AI reviews related exchange notices and news coverage to determine whether legitimate catalysts explain the activity.
If no clear fundamental reason emerges and manipulation patterns are evident, the FSS requests detailed trading records from the relevant platform for further review.
The technology also tackles wash trading and matched orders that artificially boost reported volumes.
It applies statistical techniques such as Benford’s law to examine digit distributions in trading data, then employs machine learning models including autoencoders and isolation forests to highlight periods that deviate markedly from normal behavior.
Beyond exchange data, the system monitors online channels.
Using APIs, it gathers posts, videos, and chat-room content related to digital assets.
Audio and subtitles are converted to text, after which generative AI evaluates whether the material appears intended to promote coordinated buying, spread false information, or facilitate front-running, cross-referencing these signals against actual price movements.
Once unusual activity is flagged, the platform automates much of the subsequent analysis.
It combines real-time alerts with tips, complaints, and media reports to select tokens for closer scrutiny.
AI then examines account-level metrics such as order influence, price impact, and trading profits, traces linked accounts, and identifies relevant time windows.
Generative AI subsequently drafts structured review reports that human investigators examine before deciding whether to open formal investigations.
The FSS intends to enhance the system further by incorporating tools for tracking fund flows across exchanges and monitoring on-chain blockchain activity.
Officials expressed confidence that the AI framework will enable faster, more efficient responses to increasingly sophisticated misconduct, ultimately strengthening investor protection and supporting a healthier digital asset market environment despite staffing constraints.
